CLASSIFICATION OF SENTENCES IN SPORTS NEWS AUTOMATICALLY USING ARTIFICIAL NEURAL NETWORK METHOD

  • Asep Saepul Ridwan(1*)
    Universitas Jenderal Achmad Yani
  • Yulison H Chrisnanto(2)
    Universitas Jenderal Achmad Yani
  • Ridwan Ilyas(3)
    Universitas Jenderal Achmad Yani
  • (*) Corresponding Author
Keywords: classification, artificial neural networks, backpropagation, Term Frequency-Inverse Document Frequency (TF-IDF)

Abstract

Sports news is of great interest to today's society. This is because sports have grown into entertainment. There is a lot of sports news today that covers a wide range of sports, from branches that use the ball as objects for games like football, to sports in automotive race like formula 1. Beyond that, the substance of the sports news itself is as diverse as the news of managerial from a sports club, results matches, player injuries, et cetera. Surely such a thing would be difficult. The network wants one in the field of discussion in the news. Overlap data occurs in the sports news document because it mixes one sentence data with the other. Some of the content in the sports news is about managerial, players, schedules, previews, reviews, standings, statistics, champions, etc. Becomes a problem when the reader wants a topic on the news that focuses on one particular discussion. This study has built a book on sentence classification meaning Artificial Neural Network (ANN) with a method of learning Backpropagation. The feature used is the frequency of the occurrence of a term in the corresponding sentence and the calculating result of a distributed term. The testing of our proposed methods shows an accuracy of 99% to best results on training data and 57% on test data.

Downloads

Download data is not yet available.

References

G. Tika and Adiwijaya, “Klasifikasi Topik Berita Berbahasa Indonesia menggunakan Multilayer Perceptron,” e-Proceeding Eng., vol. 6, no. 1, pp. 2137–2143, 2019.

K. M. Lhaksmana, F. Nhita, and D. Anggraini, “Klasifikasi Kepribadian Berdasarkan Status Facebook Menggunakan Metode Backpropagation,” e-Proceeding Eng., vol. 4, no. 3, pp. 5174–5183, 2017.

M. A. Assuja and Saniati “Analisis Sentimen Tweet Menggunakan Backpropagation Neural Network,” Jurnal TEKNOINFO, vol. 10, no. 2, pp. 23–28, 2016.

F. A. Shiddiq, S. Al Faraby, and Adiwijaya “Klasifikasi Sentimen Review Produk Otomotif Menggunakan Backpropagation Neural Network,” e-Proceeding of Engineering, vol. 5, no. 3, pp. 7790–7794, 2018.

R. R. M. Salim and A. S. Jauhari, “Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan,” J. SIFO Mikroskil, vol. 17, no. 1, pp. 109–118, 2016.

S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” J. JOIN, vol. 3, no. 2, pp. 110–115, 2018.

J. S. D. Raharjo, “Model Artificial Neural Network Berbasis Particle Swarm Optimization Untuk Prediksi Laju Inflasi, " J. SISKOM, vol. 3, no. 1, pp. 10–21, 2013.

N. Nikentari, H. Kurniawan, N. Ritha, and D. Kurniawan, “Optimasi Jaringan Syaraf Tiruan Backpropagation Dengan Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Laut,” J. JTIIK, vol. 5, no. 5, pp. 605-612, 2018.

L. S. Lubis and A. Buono, “Pemodelan Jaringan Syaraf Tiruan Untuk Memprediksi Awal Musim Hujan Berdasarkan Suhu Permukaan Laut Artificial Neural Network Modeling To Predict The Beginning of Rainy Season Based On Sea Surface Temperature,” J. Ilmu Komputer, vol. 1, no. 2, pp. 52-61, 2012.

Tursina, “Pendekatan Artificial Neural Network (ANN) Untuk Mengklasifikasikan Hewan Vetebrata Menggunakan Kohonen Self Organizing Map (SOM),” J. Informatika, vol. 13, no. 1, pp. 63–70, 2013.

Yunita, “Prediksi Cuaca Menggunakan Metode Neural Network,” J. Paradigma, vol. XVII, no. 2, pp. 47–53, 2015.

F. Rozi and F. Sukmana, “Penggunaan Moving Average Dengan Metode Hybrid Artificial Neural Network Dan Fuzzy Inference System Untuk Prediksi Cuaca,” J. JIPI, vol. 1, no. 02, pp. 38–42.

N. Yanti, “Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Prediksi Stok Obat Di Apotek (Studi Kasus : Apotek ABC),” J. SNATI, pp. 17–18, 2011.

Julpan, E. B. Nababan, and M. Zarlis, "Analisis Fungsi Aktivasi Sigmoid Biner Dan Sigmoid Bipolar Dalam Algoritma Backpropagation Pada Prediksi Kemampuan Siswa," J. TEKNOVASI, vol. 02, no. 1, pp. 103-116, 2015.

PlumX Metrics

Published
2021-04-02
How to Cite
[1]
A. Ridwan, Y. Chrisnanto, and R. Ilyas, “CLASSIFICATION OF SENTENCES IN SPORTS NEWS AUTOMATICALLY USING ARTIFICIAL NEURAL NETWORK METHOD”, jicon, vol. 9, no. 1, pp. 88-97, Apr. 2021.
Section
Articles

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.